{"id":"W2801276253","doi":"10.1177/0954410018773628","title":"Efficient reduced-order modeling of unsteady aerodynamics under light dynamic stall conditions","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Stall (fluid mechanics); Aerodynamics; Airfoil; Computational fluid dynamics; Pitching moment; Solver; Amplitude; Computer science; Lift (data mining); Reduced frequency; Drag; Control theory (sociology); Aeroelasticity; Angle of attack; Lift-to-drag ratio; Simulation; Mechanics; Aerospace engineering; Engineering; Turbulence; Physics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001638952,0.0005263516,0.0005456741,0.0003069115,0.0002595264,0.0004490389,0.0006857056,0.0004631203,0.0007197041],"category_scores_gemma":[0.0004965568,0.0002892665,0.000642748,0.0002177532,0.0002179152,0.0005097298,0.0002383139,0.0005709776,0.0002962552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004116183,"about_ca_system_score_gemma":0.0008154216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01361105,"about_ca_topic_score_gemma":0.01145858,"domain_scores_codex":[0.9998888,0.00001788512,0.000006272811,0.00002400744,0.00004701765,0.00001599836],"domain_scores_gemma":[0.9998555,0.00004535676,0.0000257915,0.00002036279,0.00004648097,0.000006547972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001809634,0.00001927364,0.0006325584,0.00003246927,0.00001258413,0.00003650854,0.00002730695,0.9826983,0.004980855,0.0008653391,0.0001493155,0.01052749],"study_design_scores_gemma":[4.868536e-7,0.000004002582,0.0000728932,6.17794e-7,0.000001145198,0.000002666068,0.000001199758,0.9993716,0.0003796907,0.00008429849,0.00008025826,0.000001148472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1304406,0.0004165237,0.8637559,0.0001283305,0.00004948853,0.00005470108,0.0002079632,0.0008103798,0.004136081],"genre_scores_gemma":[0.9334486,0.0003927306,0.06212784,0.00003782812,0.00002216242,0.0001108503,0.0003764898,0.0000881148,0.003395512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01361105,"threshold_uncertainty_score":0.02706367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008690823071253578,"score_gpt":0.2260440510289792,"score_spread":0.2173532279577256,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}